论文标题
包括基于图像的感知仓库无人机观察者
Including Image-based Perception in Disturbance Observer for Warehouse Drones
论文作者
论文摘要
抓握和释放对象会导致仓库中的振荡无人机。为了减少这种不想要的振荡，本文将被释放的物体视为未知的外部干扰，并提出了基于图像的干扰观察者（DOB），以估计并拒绝这种干扰。与现有的DOB技术不同，该技术只能在振荡发生后弥补扰动，而拟议的基于图像的基于图像的扰动预测将基于图像的干扰预测纳入控制循环，以进一步提高DOB的性能。提出的基于图像的DOB由两个部分组成。第一个是基于深度学习的干扰预测。通过拍摄送达对象的图像，使用连接的预训练的卷积神经网络（CNN）和较长的短期内存（LSTM）网络提前预测顺序的干扰信号。第二部分是反馈循环中的常规DOB，并进行了馈电校正，该校正利用深度学习预测来产生学习信号。进行数值研究以验证提出的基于图像的DOB，以减少对象的抓握和释放期间的递送无人机的振荡。
Grasping and releasing objects would cause oscillations to delivery drones in the warehouse. To reduce such undesired oscillations, this paper treats the to-be-delivered object as an unknown external disturbance and presents an image-based disturbance observer (DOB) to estimate and reject such disturbance. Different from the existing DOB technique that can only compensate for the disturbance after the oscillations happen, the proposed image-based one incorporates image-based disturbance prediction into the control loop to further improve the performance of the DOB. The proposed image-based DOB consists of two parts. The first one is deep-learning-based disturbance prediction. By taking an image of the to-be-delivered object, a sequential disturbance signal is predicted in advance using a connected pre-trained convolutional neural network (CNN) and a long short-term memory (LSTM) network. The second part is a conventional DOB in the feedback loop with a feedforward correction, which utilizes the deep learning prediction to generate a learning signal. Numerical studies are performed to validate the proposed image-based DOB regarding oscillation reduction for delivery drones during the grasping and releasing periods of the objects.
